Overview

Dataset statistics

Number of variables22
Number of observations9,150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory176.0 B

Variable types

Numeric14
Categorical8

Warnings

pdays is highly correlated with poutcomeHigh correlation
poutcome is highly correlated with pdaysHigh correlation
emp_var_rate is highly correlated with euribor3mHigh correlation
euribor3m is highly correlated with emp_var_rate and 1 other fieldsHigh correlation
nr_employed is highly correlated with euribor3mHigh correlation
id is uniformly distributed Uniform
id has unique values Unique
job has 1966 (21.5%) zeros Zeros
education has 1037 (11.3%) zeros Zeros
month has 5396 (59.0%) zeros Zeros
previous has 7651 (83.6%) zeros Zeros

Reproduction

Analysis started2021-01-12 07:48:42.641727
Analysis finished2021-01-12 07:49:06.277653
Duration23.64 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct9150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4575.5
Minimum1
Maximum9150
Zeros0
Zeros (%)0.0%
Memory size71.6 KiB
2021-01-12T13:19:06.389306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile458.45
Q12288.25
median4575.5
Q36862.75
95-th percentile8692.55
Maximum9150
Range9149
Interquartile range (IQR)4574.5

Descriptive statistics

Standard deviation2641.521815
Coefficient of variation (CV)0.5773187226
Kurtosis-1.2
Mean4575.5
Median Absolute Deviation (MAD)2287.5
Skewness0
Sum41865825
Variance6977637.5
MonotocityStrictly increasing
2021-01-12T13:19:06.568942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
47111
 
< 0.1%
67501
 
< 0.1%
47031
 
< 0.1%
88011
 
< 0.1%
26601
 
< 0.1%
6131
 
< 0.1%
67581
 
< 0.1%
88091
 
< 0.1%
26521
 
< 0.1%
Other values (9140)9140
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
ValueCountFrequency (%)
91501
< 0.1%
91491
< 0.1%
91481
< 0.1%
91471
< 0.1%
91461
< 0.1%

age
Real number (ℝ≥0)

Distinct75
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.7831694
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Memory size71.6 KiB
2021-01-12T13:19:06.744125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median39
Q348
95-th percentile60
Maximum98
Range81
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.66946407
Coefficient of variation (CV)0.2861343109
Kurtosis1.086646096
Mean40.7831694
Median Absolute Deviation (MAD)8
Skewness0.9221055965
Sum373166
Variance136.1763917
MonotocityNot monotonic
2021-01-12T13:19:06.936393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33394
 
4.3%
36388
 
4.2%
31382
 
4.2%
35370
 
4.0%
34356
 
3.9%
32354
 
3.9%
30336
 
3.7%
39318
 
3.5%
38309
 
3.4%
37308
 
3.4%
Other values (65)5635
61.6%
ValueCountFrequency (%)
172
 
< 0.1%
1812
 
0.1%
1920
0.2%
2026
0.3%
2130
0.3%
ValueCountFrequency (%)
982
 
< 0.1%
923
 
< 0.1%
892
 
< 0.1%
889
0.1%
871
 
< 0.1%

job
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.610819672
Minimum0
Maximum11
Zeros1966
Zeros (%)21.5%
Memory size71.6 KiB
2021-01-12T13:19:07.096713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.461752179
Coefficient of variation (CV)0.7507888891
Kurtosis-1.175199848
Mean4.610819672
Median Absolute Deviation (MAD)3
Skewness0.0736058897
Sum42189
Variance11.98372815
MonotocityNot monotonic
2021-01-12T13:19:07.239487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
72275
24.9%
01966
21.5%
21337
14.6%
5854
 
9.3%
6666
 
7.3%
11566
 
6.2%
8320
 
3.5%
1286
 
3.1%
10285
 
3.1%
9253
 
2.8%
Other values (2)342
 
3.7%
ValueCountFrequency (%)
01966
21.5%
1286
 
3.1%
21337
14.6%
3243
 
2.7%
499
 
1.1%
ValueCountFrequency (%)
11566
 
6.2%
10285
 
3.1%
9253
 
2.8%
8320
 
3.5%
72275
24.9%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
1
5602 
2
2536 
0
991 
3
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9150
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
15602
61.2%
22536
27.7%
0991
 
10.8%
321
 
0.2%
2021-01-12T13:19:07.546463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-12T13:19:07.661318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
15602
61.2%
22536
27.7%
0991
 
10.8%
321
 
0.2%

Most occurring characters

ValueCountFrequency (%)
15602
61.2%
22536
27.7%
0991
 
10.8%
321
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9150
100.0%

Most frequent character per category

ValueCountFrequency (%)
15602
61.2%
22536
27.7%
0991
 
10.8%
321
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common9150
100.0%

Most frequent character per script

ValueCountFrequency (%)
15602
61.2%
22536
27.7%
0991
 
10.8%
321
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII9150
100.0%

Most frequent character per block

ValueCountFrequency (%)
15602
61.2%
22536
27.7%
0991
 
10.8%
321
 
0.2%

education
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.635956284
Minimum0
Maximum7
Zeros1037
Zeros (%)11.3%
Memory size71.6 KiB
2021-01-12T13:19:07.739632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.913599082
Coefficient of variation (CV)0.7259600979
Kurtosis-1.324833101
Mean2.635956284
Median Absolute Deviation (MAD)1
Skewness0.2923958668
Sum24119
Variance3.661861447
MonotocityNot monotonic
2021-01-12T13:19:07.884799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
12603
28.4%
52082
22.8%
21284
14.0%
31118
12.2%
01037
 
11.3%
6534
 
5.8%
4488
 
5.3%
74
 
< 0.1%
ValueCountFrequency (%)
01037
 
11.3%
12603
28.4%
21284
14.0%
31118
12.2%
4488
 
5.3%
ValueCountFrequency (%)
74
 
< 0.1%
6534
 
5.8%
52082
22.8%
4488
 
5.3%
31118
12.2%

default
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
1
7247 
0
1903 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
17247
79.2%
01903
 
20.8%
2021-01-12T13:19:08.197755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-12T13:19:08.312191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
17247
79.2%
01903
 
20.8%

Most occurring characters

ValueCountFrequency (%)
17247
79.2%
01903
 
20.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9150
100.0%

Most frequent character per category

ValueCountFrequency (%)
17247
79.2%
01903
 
20.8%

Most occurring scripts

ValueCountFrequency (%)
Common9150
100.0%

Most frequent character per script

ValueCountFrequency (%)
17247
79.2%
01903
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII9150
100.0%

Most frequent character per block

ValueCountFrequency (%)
17247
79.2%
01903
 
20.8%

housing
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
0
4534 
1
4357 
2
 
259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9150
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
04534
49.6%
14357
47.6%
2259
 
2.8%
2021-01-12T13:19:08.542741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-12T13:19:08.658616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04534
49.6%
14357
47.6%
2259
 
2.8%

Most occurring characters

ValueCountFrequency (%)
04534
49.6%
14357
47.6%
2259
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9150
100.0%

Most frequent character per category

ValueCountFrequency (%)
04534
49.6%
14357
47.6%
2259
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common9150
100.0%

Most frequent character per script

ValueCountFrequency (%)
04534
49.6%
14357
47.6%
2259
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII9150
100.0%

Most frequent character per block

ValueCountFrequency (%)
04534
49.6%
14357
47.6%
2259
 
2.8%

loan
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
0
7579 
1
1312 
2
 
259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9150
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
07579
82.8%
11312
 
14.3%
2259
 
2.8%
2021-01-12T13:19:08.912201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-12T13:19:09.026803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
07579
82.8%
11312
 
14.3%
2259
 
2.8%

Most occurring characters

ValueCountFrequency (%)
07579
82.8%
11312
 
14.3%
2259
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9150
100.0%

Most frequent character per category

ValueCountFrequency (%)
07579
82.8%
11312
 
14.3%
2259
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common9150
100.0%

Most frequent character per script

ValueCountFrequency (%)
07579
82.8%
11312
 
14.3%
2259
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII9150
100.0%

Most frequent character per block

ValueCountFrequency (%)
07579
82.8%
11312
 
14.3%
2259
 
2.8%

contact
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
0
5297 
1
3853 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
05297
57.9%
13853
42.1%
2021-01-12T13:19:09.372931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-12T13:19:09.489230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
05297
57.9%
13853
42.1%

Most occurring characters

ValueCountFrequency (%)
05297
57.9%
13853
42.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9150
100.0%

Most frequent character per category

ValueCountFrequency (%)
05297
57.9%
13853
42.1%

Most occurring scripts

ValueCountFrequency (%)
Common9150
100.0%

Most frequent character per script

ValueCountFrequency (%)
05297
57.9%
13853
42.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9150
100.0%

Most frequent character per block

ValueCountFrequency (%)
05297
57.9%
13853
42.1%

month
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.775300546
Minimum0
Maximum9
Zeros5396
Zeros (%)59.0%
Memory size71.6 KiB
2021-01-12T13:19:09.569859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.718473696
Coefficient of variation (CV)1.531275198
Kurtosis0.7012844458
Mean1.775300546
Median Absolute Deviation (MAD)0
Skewness1.419172227
Sum16244
Variance7.390099237
MonotocityNot monotonic
2021-01-12T13:19:09.712249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
05396
59.0%
3655
 
7.2%
2649
 
7.1%
1559
 
6.1%
8539
 
5.9%
5416
 
4.5%
4315
 
3.4%
7276
 
3.0%
9256
 
2.8%
689
 
1.0%
ValueCountFrequency (%)
05396
59.0%
1559
 
6.1%
2649
 
7.1%
3655
 
7.2%
4315
 
3.4%
ValueCountFrequency (%)
9256
2.8%
8539
5.9%
7276
3.0%
689
 
1.0%
5416
4.5%

day_of_week
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
1
2137 
3
1901 
0
1768 
2
1750 
4
1594 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9150
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
12137
23.4%
31901
20.8%
01768
19.3%
21750
19.1%
41594
17.4%
2021-01-12T13:19:10.014953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-12T13:19:10.131348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
12137
23.4%
31901
20.8%
01768
19.3%
21750
19.1%
41594
17.4%

Most occurring characters

ValueCountFrequency (%)
12137
23.4%
31901
20.8%
01768
19.3%
21750
19.1%
41594
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9150
100.0%

Most frequent character per category

ValueCountFrequency (%)
12137
23.4%
31901
20.8%
01768
19.3%
21750
19.1%
41594
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common9150
100.0%

Most frequent character per script

ValueCountFrequency (%)
12137
23.4%
31901
20.8%
01768
19.3%
21750
19.1%
41594
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII9150
100.0%

Most frequent character per block

ValueCountFrequency (%)
12137
23.4%
31901
20.8%
01768
19.3%
21750
19.1%
41594
17.4%

duration
Real number (ℝ≥0)

Distinct1405
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.0501639
Minimum3
Maximum4199
Zeros0
Zeros (%)0.0%
Memory size71.6 KiB
2021-01-12T13:19:10.240530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile71
Q1166
median282
Q3533.75
95-th percentile1102.65
Maximum4199
Range4196
Interquartile range (IQR)367.75

Descriptive statistics

Standard deviation356.9098545
Coefficient of variation (CV)0.8855221667
Kurtosis8.902702165
Mean403.0501639
Median Absolute Deviation (MAD)146.5
Skewness2.272913247
Sum3687909
Variance127384.6442
MonotocityNot monotonic
2021-01-12T13:19:10.418792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16634
 
0.4%
16032
 
0.3%
13631
 
0.3%
21131
 
0.3%
13931
 
0.3%
17930
 
0.3%
19430
 
0.3%
19230
 
0.3%
18229
 
0.3%
20729
 
0.3%
Other values (1395)8843
96.6%
ValueCountFrequency (%)
31
< 0.1%
42
< 0.1%
52
< 0.1%
61
< 0.1%
72
< 0.1%
ValueCountFrequency (%)
41991
< 0.1%
36431
< 0.1%
36311
< 0.1%
33661
< 0.1%
31831
< 0.1%

campaign
Real number (ℝ≥0)

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.201857923
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Memory size71.6 KiB
2021-01-12T13:19:10.577390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.119971716
Coefficient of variation (CV)0.9628104035
Kurtosis90.39525685
Mean2.201857923
Median Absolute Deviation (MAD)1
Skewness6.37810372
Sum20147
Variance4.494280076
MonotocityNot monotonic
2021-01-12T13:19:10.736407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
14254
46.5%
22528
27.6%
31124
 
12.3%
4532
 
5.8%
5250
 
2.7%
6150
 
1.6%
798
 
1.1%
945
 
0.5%
843
 
0.5%
1035
 
0.4%
Other values (17)91
 
1.0%
ValueCountFrequency (%)
14254
46.5%
22528
27.6%
31124
 
12.3%
4532
 
5.8%
5250
 
2.7%
ValueCountFrequency (%)
561
< 0.1%
421
< 0.1%
391
< 0.1%
351
< 0.1%
251
< 0.1%

pdays
Real number (ℝ≥0)

HIGH CORRELATION

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean894.047541
Minimum0
Maximum999
Zeros10
Zeros (%)0.1%
Memory size71.6 KiB
2021-01-12T13:19:10.895178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation305.325311
Coefficient of variation (CV)0.3415090328
Kurtosis4.584143307
Mean894.047541
Median Absolute Deviation (MAD)0
Skewness-2.565731251
Sum8180535
Variance93223.54556
MonotocityNot monotonic
2021-01-12T13:19:11.057805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
9998183
89.4%
3298
 
3.3%
6289
 
3.2%
463
 
0.7%
740
 
0.4%
237
 
0.4%
935
 
0.4%
1030
 
0.3%
529
 
0.3%
1328
 
0.3%
Other values (16)118
 
1.3%
ValueCountFrequency (%)
010
 
0.1%
18
 
0.1%
237
 
0.4%
3298
3.3%
463
 
0.7%
ValueCountFrequency (%)
9998183
89.4%
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
222
 
< 0.1%

previous
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2498360656
Minimum0
Maximum6
Zeros7651
Zeros (%)83.6%
Memory size71.6 KiB
2021-01-12T13:19:11.203268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6602955742
Coefficient of variation (CV)2.642915356
Kurtosis13.31496439
Mean0.2498360656
Median Absolute Deviation (MAD)0
Skewness3.340200299
Sum2286
Variance0.4359902453
MonotocityNot monotonic
2021-01-12T13:19:11.348801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
07651
83.6%
1967
 
10.6%
2350
 
3.8%
3128
 
1.4%
438
 
0.4%
513
 
0.1%
63
 
< 0.1%
ValueCountFrequency (%)
07651
83.6%
1967
 
10.6%
2350
 
3.8%
3128
 
1.4%
438
 
0.4%
ValueCountFrequency (%)
63
 
< 0.1%
513
 
0.1%
438
 
0.4%
3128
 
1.4%
2350
3.8%

poutcome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
0
7651 
2
894 
1
 
605

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9150
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
07651
83.6%
2894
 
9.8%
1605
 
6.6%
2021-01-12T13:19:11.663599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-12T13:19:11.776906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
07651
83.6%
2894
 
9.8%
1605
 
6.6%

Most occurring characters

ValueCountFrequency (%)
07651
83.6%
2894
 
9.8%
1605
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9150
100.0%

Most frequent character per category

ValueCountFrequency (%)
07651
83.6%
2894
 
9.8%
1605
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common9150
100.0%

Most frequent character per script

ValueCountFrequency (%)
07651
83.6%
2894
 
9.8%
1605
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII9150
100.0%

Most frequent character per block

ValueCountFrequency (%)
07651
83.6%
2894
 
9.8%
1605
 
6.6%

emp_var_rate
Real number (ℝ)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.08330054645
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Memory size71.6 KiB
2021-01-12T13:19:11.849850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-3
Q1-1.8
median1.1
Q31.1
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation1.64249331
Coefficient of variation (CV)-19.7176775
Kurtosis-1.077937633
Mean-0.08330054645
Median Absolute Deviation (MAD)0
Skewness-0.758342162
Sum-762.2
Variance2.697784274
MonotocityNot monotonic
2021-01-12T13:19:11.993896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.14750
51.9%
-1.81461
 
16.0%
1.4866
 
9.5%
-2.9594
 
6.5%
-3.4454
 
5.0%
-1.7403
 
4.4%
-1.1301
 
3.3%
-0.1232
 
2.5%
-388
 
1.0%
-0.21
 
< 0.1%
ValueCountFrequency (%)
-3.4454
 
5.0%
-388
 
1.0%
-2.9594
6.5%
-1.81461
16.0%
-1.7403
 
4.4%
ValueCountFrequency (%)
1.4866
 
9.5%
1.14750
51.9%
-0.1232
 
2.5%
-0.21
 
< 0.1%
-1.1301
 
3.3%

cons_price_idx
Real number (ℝ≥0)

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.66964929
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Memory size71.6 KiB
2021-01-12T13:19:12.137764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.431
Q193.2
median93.994
Q393.994
95-th percentile94.215
Maximum94.767
Range2.566
Interquartile range (IQR)0.794

Descriptive statistics

Standard deviation0.578289085
Coefficient of variation (CV)0.006173708232
Kurtosis-0.03084718508
Mean93.66964929
Median Absolute Deviation (MAD)0
Skewness-1.033532813
Sum857077.291
Variance0.3344182658
MonotocityNot monotonic
2021-01-12T13:19:12.297153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9944750
51.9%
92.893524
 
5.7%
93.075442
 
4.8%
93.918407
 
4.4%
93.444271
 
3.0%
92.963264
 
2.9%
92.201264
 
2.9%
93.2190
 
2.1%
94.465188
 
2.1%
92.431180
 
2.0%
Other values (16)1670
 
18.3%
ValueCountFrequency (%)
92.201264
2.9%
92.379106
1.2%
92.431180
2.0%
92.46966
 
0.7%
92.649168
1.8%
ValueCountFrequency (%)
94.76758
 
0.6%
94.60193
1.0%
94.465188
2.1%
94.215176
1.9%
94.199150
1.6%

cons_conf_idx
Real number (ℝ)

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-38.11897268
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Memory size71.6 KiB
2021-01-12T13:19:12.447756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-40.8
median-36.4
Q3-36.4
95-th percentile-31.4
Maximum-26.9
Range23.9
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation4.688913004
Coefficient of variation (CV)-0.1230073288
Kurtosis0.5563142862
Mean-38.11897268
Median Absolute Deviation (MAD)0
Skewness-0.6323721873
Sum-348788.6
Variance21.98590516
MonotocityNot monotonic
2021-01-12T13:19:12.607937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.44750
51.9%
-46.2524
 
5.7%
-47.1442
 
4.8%
-42.7407
 
4.4%
-36.1271
 
3.0%
-31.4264
 
2.9%
-40.8264
 
2.9%
-42190
 
2.1%
-41.8188
 
2.1%
-26.9180
 
2.0%
Other values (16)1670
 
18.3%
ValueCountFrequency (%)
-50.858
 
0.6%
-50126
 
1.4%
-49.593
 
1.0%
-47.1442
4.8%
-46.2524
5.7%
ValueCountFrequency (%)
-26.9180
2.0%
-29.8106
1.2%
-30.1168
1.8%
-31.4264
2.9%
-3388
 
1.0%

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION

Distinct287
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.470681858
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Memory size71.6 KiB
2021-01-12T13:19:13.143002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.714
Q11.266
median4.856
Q34.858
95-th percentile4.962
Maximum5.045
Range4.411
Interquartile range (IQR)3.592

Descriptive statistics

Standard deviation1.846120835
Coefficient of variation (CV)0.531918773
Kurtosis-1.594647197
Mean3.470681858
Median Absolute Deviation (MAD)0.004
Skewness-0.5981945781
Sum31756.739
Variance3.408162139
MonotocityNot monotonic
2021-01-12T13:19:13.320707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8561210
 
13.2%
4.8571071
 
11.7%
4.855840
 
9.2%
4.859788
 
8.6%
4.86471
 
5.1%
4.858352
 
3.8%
4.962144
 
1.6%
1.365136
 
1.5%
1.405135
 
1.5%
4.963126
 
1.4%
Other values (277)3877
42.4%
ValueCountFrequency (%)
0.6346
 
0.1%
0.63522
0.2%
0.6363
 
< 0.1%
0.6375
 
0.1%
0.6385
 
0.1%
ValueCountFrequency (%)
5.0455
 
0.1%
53
 
< 0.1%
4.974
 
< 0.1%
4.96857
0.6%
4.96733
0.4%

nr_employed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5142.376852
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Memory size71.6 KiB
2021-01-12T13:19:13.479074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile4991.6
Q15099.1
median5191
Q35191
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)91.9

Descriptive statistics

Standard deviation78.65603932
Coefficient of variation (CV)0.01529565833
Kurtosis-0.5664451328
Mean5142.376852
Median Absolute Deviation (MAD)0
Skewness-0.9319604173
Sum47052748.2
Variance6186.772521
MonotocityNot monotonic
2021-01-12T13:19:13.627516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
51914750
51.9%
5099.11092
 
11.9%
5228.1866
 
9.5%
5076.2594
 
6.5%
5017.5454
 
5.0%
4991.6403
 
4.4%
5008.7369
 
4.0%
4963.6301
 
3.3%
5195.8232
 
2.5%
5023.588
 
1.0%
ValueCountFrequency (%)
4963.6301
3.3%
4991.6403
4.4%
5008.7369
4.0%
5017.5454
5.0%
5023.588
 
1.0%
ValueCountFrequency (%)
5228.1866
 
9.5%
5195.8232
 
2.5%
51914750
51.9%
5176.31
 
< 0.1%
5099.11092
 
11.9%

y
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
1
4640 
0
4510 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9150
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
14640
50.7%
04510
49.3%
2021-01-12T13:19:13.933872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-01-12T13:19:14.062917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
14640
50.7%
04510
49.3%

Most occurring characters

ValueCountFrequency (%)
14640
50.7%
04510
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9150
100.0%

Most frequent character per category

ValueCountFrequency (%)
14640
50.7%
04510
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common9150
100.0%

Most frequent character per script

ValueCountFrequency (%)
14640
50.7%
04510
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9150
100.0%

Most frequent character per block

ValueCountFrequency (%)
14640
50.7%
04510
49.3%

Interactions

2021-01-12T13:18:46.744462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:46.852348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:46.950401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:47.049261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:47.240943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:47.334904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:47.430219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:47.527804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:47.625622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:47.720187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:47.815675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:47.911301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:48.004961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:48.100433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:48.203954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:48.309628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:48.418017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:48.522990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:48.628156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:48.735951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:48.842027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:48.949563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:49.053962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:49.158619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:49.263228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:49.368772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:49.474702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:49.571378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:49.674960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:49.776912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:49.872653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:49.970473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:50.068054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:50.166803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:50.375569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:50.473497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:50.573497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:50.671436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:50.768742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:50.865982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:50.967020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:51.075579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:51.181131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:51.282619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:51.383924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:51.488482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:51.592222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:51.699818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:51.804051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:51.907150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.009794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.111478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.213516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.307308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.408776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.504262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.603373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.697981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.793909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.889316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:52.986780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:53.081472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:53.176688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:53.271227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:53.365544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:53.459894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:53.554743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:53.656220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:53.751254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:53.849816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:53.943638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:54.038938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:54.281427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:54.380163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:54.475822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:54.571495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:54.666737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:54.760849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:54.855287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:54.951799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:55.056447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:55.154527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:55.256392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:55.352720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:55.449172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:55.548692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:55.648981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:55.746433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:55.844292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:55.941839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:56.038619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:56.135708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:56.231480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:56.334799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:56.439137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:56.541142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:56.636681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:56.733210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:56.831378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:56.931590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:57.028533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:57.126184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:57.223718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:57.320043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:57.416595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:57.516352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:57.622277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:57.722823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:57.827263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:57.926115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:58.024678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:58.124796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:58.225687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:58.324913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:58.424788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:58.525291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:58.623904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:58.723262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:58.818172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:59.121595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:59.219316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:59.320161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:59.415033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:59.510551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:59.607581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:59.704501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:59.801762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:59.897144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:18:59.991487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:00.088830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:00.184624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:00.280444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:00.383276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:00.481424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:00.582870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:00.678417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:00.774742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:00.872090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:00.969601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:01.067991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:01.163134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:01.258984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:01.353781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:01.451958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:01.548091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:01.650405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:01.748400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:01.848562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:01.943444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:02.038788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:02.135582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:02.232211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:02.329860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:02.424561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:02.520462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:02.615273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:02.714194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:02.808966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:02.911942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.008253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.108669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.203261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.298442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.394744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.492515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.590753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.685978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.781364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.876134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:03.971273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:04.066680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:04.169721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:04.266690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:04.367253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:04.462939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:04.558998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:04.656399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:04.754022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:05.122153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:05.219028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:05.314828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-01-12T13:19:05.410208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-01-12T13:19:14.256446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-12T13:19:14.472623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-12T13:19:14.674635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-12T13:19:14.877173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-12T13:19:15.043766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-12T13:19:05.633024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-12T13:19:06.053222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idagejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp_var_ratecons_price_idxcons_conf_idxeuribor3mnr_employedy
014100000000015751999001.193.994-36.44.8575191.01
124911100000010421999001.193.994-36.44.8575191.01
234921211000014671999001.193.994-36.44.8575191.01
34412130000005791999001.193.994-36.44.8575191.01
45450120000004611999001.193.994-36.44.8575191.01
56420121010006732999001.193.994-36.44.8575191.01
67393121000009353999001.193.994-36.44.8575191.01
782842400100112011999001.193.994-36.44.8575191.01
894451510000110301999001.193.994-36.44.8575191.01
9104221311000116231999001.193.994-36.44.8575191.01

Last rows

idagejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp_var_ratecons_price_idxcons_conf_idxeuribor3mnr_employedy
91409141427220110024081999001.193.994-36.44.8585191.00
9141914260315000002741999001.193.994-36.44.8585191.00
9142914335212110002241999001.193.994-36.44.8585191.00
91439144457111010023691999001.193.994-36.44.8585191.00
91449145360161000021671999001.193.994-36.44.8585191.00
9145914633912100002643999001.193.994-36.44.8585191.00
91469147546060000021511999001.193.994-36.44.8585191.00
91479148463101100023361999001.193.994-36.44.8585191.00
9148914932621110002591999001.193.994-36.44.8585191.00
9149915042000110002931999001.193.994-36.44.8585191.00